School of Nursing, University of Minnesota, 5-140 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN, 55455, United States, 16126245959.
Center for Digital Health, Mayo Clinic, Rochester, MN, United States.
JMIR Nurs. 2024 Oct 25;7:e62678. doi: 10.2196/62678.
The ethics of artificial intelligence (AI) are increasingly recognized due to concerns such as algorithmic bias, opacity, trust issues, data security, and fairness. Specifically, machine learning algorithms, central to AI technologies, are essential in striving for ethically sound systems that mimic human intelligence. These technologies rely heavily on data, which often remain obscured within complex systems and must be prioritized for ethical collection, processing, and usage. The significance of data ethics in achieving responsible AI was first highlighted in the broader context of health care and subsequently in nursing. This viewpoint explores the principles of data ethics, drawing on relevant frameworks and strategies identified through a formal literature review. These principles apply to real-world and synthetic data in AI and machine-learning contexts. Additionally, the data-centric AI paradigm is briefly examined, emphasizing its focus on data quality and the ethical development of AI solutions that integrate human-centered domain expertise. The ethical considerations specific to nursing are addressed, including 4 recommendations for future directions in nursing practice, research, and education and 2 hypothetical nurse-focused ethical case studies. The primary objectives are to position nurses to actively participate in AI and data ethics, thereby contributing to creating high-quality and relevant data for machine learning applications.
人工智能（AI）的伦理道德日益受到关注，例如算法偏见、不透明性、信任问题、数据安全和公平性。具体来说，机器学习算法是 AI 技术的核心，对于追求符合道德规范的系统以模拟人类智能至关重要。这些技术严重依赖数据，而数据往往隐藏在复杂的系统中，必须优先考虑进行合乎道德的数据收集、处理和使用。数据伦理在实现负责任的 AI 方面的重要性首先在更广泛的医疗保健背景下得到强调，随后在护理领域也得到了强调。本观点探讨了数据伦理原则，借鉴了通过正式文献回顾确定的相关框架和策略。这些原则适用于 AI 和机器学习环境中的真实数据和合成数据。此外，还简要探讨了以数据为中心的 AI 范式，强调了其对数据质量的关注以及将以人为中心的领域专业知识纳入 AI 解决方案的伦理开发。还讨论了护理方面的具体伦理考虑因素，包括针对护理实践、研究和教育的 4 项未来方向建议，以及 2 个以护士为重点的假设性伦理案例研究。主要目标是使护士能够积极参与 AI 和数据伦理，从而为机器学习应用程序创建高质量和相关的数据。
